It has been proved feasible to generate attenuation maps (mu-maps) from cardiac SPECT using deep learning. However, this assumed that the training and testing datasets were acquired using the same scanner, tracer, and protocol. We investigated a robust generation of CT-derived mu-maps from cardiac SPECT acquired by different scanners, tracers, and protocols from the training data. We first pre-trained a network using 120 studies injected with (99m)Tc-tetrofosmin acquired from a GE 850 SPECT/CT with 360-degree gantry rotation, which was then fine-tuned and tested using 80 studies injected with (99m)Tc-sestamibi acquired from a Philips BrightView SPECT/CT with 180-degree gantry rotation. The error between ground-truth and predicted mu-maps by transfer learning was 5.13 +/- 7.02%, as compared to 8.24 +/- 5.01% by direct transition without fine-tuning and 6.45 +/- 5.75% by limited-sample training. The error between ground-truth and reconstructed images with predicted mu-maps by transfer learning was 1.11 +/- 1.57%, as compared to 1.72 +/- 1.63% by direct transition and 1.68 +/- 1.21% by limited-sample training. It is feasible to apply a network pre-trained by a large amount of data from one scanner to data acquired by another scanner using different tracers and protocols, with proper transfer learning.